{"id":"W2922284545","doi":"10.1117/12.2512481","title":"Mechanically controlled spectroscopic imaging for tissue classification","year":2019,"lang":"en","type":"article","venue":"","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Software; Computer science; Biomedical engineering; Artificial intelligence; Tissue sample; Computer vision; Breast tissue; Raman spectroscopy; Image registration; Sample (material); Landmark; Materials science; Pattern recognition (psychology); Medicine; Optics; Image (mathematics); Chemistry; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007581096,0.000582564,0.0003513332,0.001092096,0.0003738924,0.0006799556,0.000939156,0.0007477443,0.00346178],"category_scores_gemma":[0.001788155,0.0003602136,0.0005168338,0.0006704116,0.0006212548,0.0006187048,0.0005370309,0.000504181,0.001484246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000682564,"about_ca_system_score_gemma":0.0006028538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001468664,"about_ca_topic_score_gemma":0.003028833,"domain_scores_codex":[0.9991709,0.000119111,0.00003658146,0.0001715693,0.0004670243,0.00003479869],"domain_scores_gemma":[0.9991155,0.0002596579,0.0002125991,0.0001528714,0.0002304238,0.00002889849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001135682,0.00004102917,0.00196359,0.0001792003,0.00002962955,0.00005412406,0.00005475443,0.003506099,0.8819773,0.0008356833,0.0009315548,0.1103134],"study_design_scores_gemma":[0.00004002247,0.0004457479,0.02327991,0.00008950201,0.000113254,0.00119005,0.00009465664,0.1877284,0.7637699,0.002187337,0.02094595,0.0001153305],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1158256,0.002447506,0.8717326,0.0004836448,0.0001614643,0.0002517368,0.0003911337,0.004461976,0.004244285],"genre_scores_gemma":[0.3973925,0.001142778,0.5965773,0.0002310042,0.00008599876,0.0002057131,0.0004927412,0.0003955916,0.003476282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00346178,"threshold_uncertainty_score":0.01158082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00988629768860668,"score_gpt":0.3375442816828929,"score_spread":0.3276579839942862,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}